




"""simple docstring"""

import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   List[str]				)       ->   str:






      '''simple docstring'''


      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
      _A     =  Image.open(requests.get(_snake_case							,    stream=_snake_case				).raw				).convert('RGB'				)

      _A     =  transforms.Compose(
          [
              transforms.Resize((image_size, image_size)							,    interpolation=InterpolationMode.BICUBIC				),
              transforms.ToTensor(),
              transforms.Normalize((0.48145466, 0.4578275, 0.40821073)							,    (0.26862954, 0.26130258, 0.27577711)				),
          ]				)
      _A     =  transform(_snake_case				).unsqueeze(0				).to(_snake_case				)
      return image






def 		_snake_case	(   _snake_case				:   List[str]				)       ->   Dict:






      '''simple docstring'''


      if "visual_encoder" in key:
            _A     =  re.sub('visual_encoder*'							,    'vision_model.encoder'							,    _snake_case				)
      if "blocks" in key:
            _A     =  re.sub(R'blocks'							,    'layers'							,    _snake_case				)
      if "attn" in key:
            _A     =  re.sub(R'attn'							,    'self_attn'							,    _snake_case				)
      if "norm1" in key:
            _A     =  re.sub(R'norm1'							,    'layer_norm1'							,    _snake_case				)
      if "norm2" in key:
            _A     =  re.sub(R'norm2'							,    'layer_norm2'							,    _snake_case				)
      if "encoder.norm" in key:
            _A     =  re.sub(R'encoder.norm'							,    'post_layernorm'							,    _snake_case				)
      if "encoder.patch_embed.proj" in key:
            _A     =  re.sub(R'encoder.patch_embed.proj'							,    'embeddings.patch_embedding'							,    _snake_case				)

      if "encoder.pos_embed" in key:
            _A     =  re.sub(R'encoder.pos_embed'							,    'embeddings.position_embedding'							,    _snake_case				)
      if "encoder.cls_token" in key:
            _A     =  re.sub(R'encoder.cls_token'							,    'embeddings.class_embedding'							,    _snake_case				)

      if "self_attn" in key:
            _A     =  re.sub(R'self_attn.proj'							,    'self_attn.projection'							,    _snake_case				)

      return key






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   List[str]=None				)       ->   Any:






      '''simple docstring'''


      if config_path is not None:
            _A     =  BlipConfig.from_pretrained(_snake_case				)
      else:
            _A     =  BlipConfig(projection_dim=5_12							,    text_config={}							,    vision_config={}				)

      _A     =  BlipForConditionalGeneration(_snake_case				).eval()

      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

      _A     =  blip_decoder(pretrained=_snake_case							,    image_size=3_84							,    vit='base'				)
      _A     =  pt_model.eval()

      _A     =  pt_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      hf_model.load_state_dict(_snake_case				)

      _A     =  3_84
      _A     =  load_demo_image(image_size=_snake_case							,    device='cpu'				)
      _A     =  BertTokenizer.from_pretrained('bert-base-uncased'				)
      _A     =  tokenizer(['a picture of']				).input_ids

      _A     =  hf_model.generate(_snake_case							,    _snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      _A     =  hf_model.generate(_snake_case				)

      assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

      if pytorch_dump_folder_path is not None:
            hf_model.save_pretrained(_snake_case				)

      # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
      _A     =  (
          'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
      )

      _A     =  blip_vqa(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      vqa_model.eval()

      _A     =  vqa_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForQuestionAnswering(_snake_case				)

      hf_vqa_model.load_state_dict(_snake_case				)

      _A     =  ['How many dogs are in this image?']
      _A     =  tokenizer(_snake_case							,    return_tensors='pt'				).input_ids

      _A     =  hf_vqa_model.generate(_snake_case							,    _snake_case				)
      print(tokenizer.decode(answer[0]				)				)

      assert tokenizer.decode(answer[0]				) == "[UNK] 1 [SEP]"
      if pytorch_dump_folder_path is not None:
            hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'				)

      _A     =  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

      _A     =  blip_itm(pretrained=_snake_case							,    image_size=_snake_case							,    vit='base'				)
      itm_model.eval()

      _A     =  itm_model.state_dict()
      for key in modified_state_dict.copy():
            _A     =  modified_state_dict.pop(_snake_case				)
            _A     =  rename_key(_snake_case				)
            _A     =  value

      _A     =  BlipForImageTextRetrieval(_snake_case				)

      _A     =  ['A picture of a woman with a dog sitting in a beach']
      _A     =  tokenizer(
          _snake_case							,    return_tensors='pt'							,    padding='max_length'							,    truncation=_snake_case							,    max_length=35							,    ).input_ids

      hf_itm_model.load_state_dict(_snake_case				)
      hf_itm_model.eval()

      _A     =  hf_itm_model(_snake_case							,    _snake_case							,    use_itm_head=_snake_case				)
      _A     =  hf_itm_model(_snake_case							,    _snake_case							,    use_itm_head=_snake_case				)

      assert out[0].item() == 0.2110687494277954
      assert torch.nn.functional.softmax(out_itm[0]							,    dim=1				)[:, 1].item() == 0.45698845386505127

      if pytorch_dump_folder_path is not None:
            hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       a							      =     parser.parse_args()

       convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase									=   random.Random()






def   __A    (				a_    :	Union[str, Any]   ,a_    :	Tuple=1.0   ,a_    :	Optional[int]=None   ,a_    :	Union[str, Any]=None						):
		if rng is None:
				lowerCAmelCase  :     str       						= global_rng

		lowerCAmelCase  :     Any       						= []
		for batch_idx in range(shape[0]						):
				values.append([]						)
				for _ in range(shape[1]						):
						values[-1].append(rng.random() * scale						)

		return values
class 		lowerCamelCase	(   unittest.TestCase							):



	def __init__( self		,   a_		,   a_=7		,   a_=400		,   a_=2_000		,   a_=1		,   a_=0.0		,   a_=16_000		,   a_=True		,   a_=True		,   ):
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     Optional[int]       						= batch_size
			lowerCAmelCase  :     Dict       						= min_seq_length
			lowerCAmelCase  :     Dict       						= max_seq_length
			lowerCAmelCase  :     Optional[Any]       						= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCAmelCase  :     Union[str, Any]       						= feature_size
			lowerCAmelCase  :     Tuple       						= padding_value
			lowerCAmelCase  :     Dict       						= sampling_rate
			lowerCAmelCase  :     int       						= return_attention_mask
			lowerCAmelCase  :     Optional[Any]       						= do_normalize



	def 	_lowerCamelCase     ( self						):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			    "do_normalize": self.do_normalize,
			}





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=False						):
			def _flatten(a_						):
					return list(itertools.chain(*a_						)						)

			if equal_length:
					lowerCAmelCase  :     List[Any]       						= floats_list((self.batch_size, self.max_seq_length)						)
			else:
					# make sure that inputs increase in size
					lowerCAmelCase  :     Any       						= [
					    _flatten(floats_list((x, self.feature_size)						)						)
					    for x in range(self.min_seq_length		,   self.max_seq_length		,   self.seq_length_diff						)
					]

			if numpify:
					lowerCAmelCase  :     List[str]       						= [np.asarray(a_						) for x in speech_inputs]

			return speech_inputs






class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = WavaVecaFeatureExtractor



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= WavaVecaFeatureExtractionTester(self						)



	def 	_lowerCamelCase     ( self		,   a_						):
			self.assertTrue(np.all(np.mean(a_		,   axis=0						) < 1e-3						)						)
			self.assertTrue(np.all(np.abs(np.var(a_		,   axis=0						) - 1						) < 1e-3						)						)



	def 	_lowerCamelCase     ( self						):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCAmelCase  :     int       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			# create three inputs of length 800, 1000, and 1200
			lowerCAmelCase  :     str       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Dict       						= [np.asarray(a_						) for speech_input in speech_inputs]

			# Test not batched input
			lowerCAmelCase  :     Optional[int]       						= feat_extract(speech_inputs[0]		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     Any       						= feat_extract(np_speech_inputs[0]		,   return_tensors="np"						).input_values
			self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)

			# Test batched
			lowerCAmelCase  :     Any       						= feat_extract(a_		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     int       						= feat_extract(a_		,   return_tensors="np"						).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,   a_						):
					self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)

			# Test 2-D numpy arrays are batched.
			lowerCAmelCase  :     Dict       						= [floats_list((1, x)						)[0] for x in (800, 800, 800)]
			lowerCAmelCase  :     Tuple       						= np.asarray(a_						)
			lowerCAmelCase  :     int       						= feat_extract(a_		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(a_		,   return_tensors="np"						).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,   a_						):
					self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Any       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]

			lowerCAmelCase  :     Union[str, Any]       						= ["longest", "max_length", "do_not_pad"]
			lowerCAmelCase  :     Optional[int]       						= [None, 1_600, None]
			for max_length, padding in zip(a_		,   a_						):
					lowerCAmelCase  :     List[Any]       						= feat_extract(a_		,   padding=a_		,   max_length=a_		,   return_tensors="np"						)
					lowerCAmelCase  :     Any       						= processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:800]						)
					self.assertTrue(input_values[0][800:].sum() < 1e-6						)
					self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
					self.assertTrue(input_values[0][1_000:].sum() < 1e-6						)
					self._check_zero_mean_unit_variance(input_values[2][:1_200]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Optional[int]       						= range(800		,   1_400		,   200						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in lengths]

			lowerCAmelCase  :     str       						= ["longest", "max_length", "do_not_pad"]
			lowerCAmelCase  :     Any       						= [None, 1_600, None]

			for max_length, padding in zip(a_		,   a_						):
					lowerCAmelCase  :     Dict       						= feat_extract(a_		,   max_length=a_		,   padding=a_						)
					lowerCAmelCase  :     int       						= processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:800]						)
					self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
					self._check_zero_mean_unit_variance(input_values[2][:1_200]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Dict       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=1_000		,   padding="max_length"		,   return_tensors="np"						)
			lowerCAmelCase  :     Union[str, Any]       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1]						)
			self._check_zero_mean_unit_variance(input_values[2]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=1_000		,   padding="longest"		,   return_tensors="np"						)
			lowerCAmelCase  :     Dict       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
			self._check_zero_mean_unit_variance(input_values[2]						)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertTrue(input_values.shape == (3, 1_000)						)

			lowerCAmelCase  :     List[str]       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=2_000		,   padding="longest"		,   return_tensors="np"						)
			lowerCAmelCase  :     Optional[Any]       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
			self._check_zero_mean_unit_variance(input_values[2]						)

			# make sure that if max_length > longest -> then pad to longest
			self.assertTrue(input_values.shape == (3, 1_200)						)



	@require_torch
	def 	_lowerCamelCase     ( self						):
			import torch

			lowerCAmelCase  :     Optional[int]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     List[Any]       						= np.random.rand(100						).astype(np.floataa						)
			lowerCAmelCase  :     List[Any]       						= np_speech_inputs.tolist()

			for inputs in [py_speech_inputs, np_speech_inputs]:
					lowerCAmelCase  :     Union[str, Any]       						= feature_extractor.pad([{"input_values": inputs}]		,   return_tensors="np"						)
					self.assertTrue(np_processed.input_values.dtype == np.floataa						)
					lowerCAmelCase  :     Tuple       						= feature_extractor.pad([{"input_values": inputs}]		,   return_tensors="pt"						)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)





	@slow
	@require_torch
	def 	_lowerCamelCase     ( self						):
			# this test makes sure that models that are using
			# group norm don't have their feature extractor return the
			# attention_mask
			for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
					lowerCAmelCase  :     Any       						= WavaVecaConfig.from_pretrained(a_						)
					lowerCAmelCase  :     List[str]       						= WavaVecaFeatureExtractor.from_pretrained(a_						)

					# only "layer" feature extraction norm should make use of
					# attention_mask
					self.assertEqual(feat_extract.return_attention_mask		,   config.feat_extract_norm == "layer"						)



